Automotive Design Research Articles

Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP

This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework’s effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications.

Investigating the impacts of auditory and visual feedback in advanced driver assistance systems: a pilot study on driver behavior and emotional response

In the autonomous vehicle industry, Advanced Driver Assistance Systems (ADAS) are recognized for their capacity to enhance service quality, improve on-road safety, and increase driver comfort. Driver Assistance Systems are able to provide multi-modal feedback including auditory cues, visual cues, vibrotactile cues and so on. The study will concentrate on assessing the impacts of auditory and visual feedback from assistive driving systems on drivers. A group consisting of five participants (N = 5) was recruited to take part in two sets of driving experiments. During the experimental sessions, they were exposed to several reminders designed for drivers in audio-only format and audio-visual format, respectively. Their driving behaviors and performances were under researcher’s observation, while their emotions were evaluated by YOLO v5 detecting model. The results reveal that the participants higher compliance rate and strong emotional reactions (especially the feelings of anger, sadness and surprise) towards the unimodal feedback of audio-only driving reminders. There is no strong evidence showing that the bimodal ADAS feedback of audio-visual cues effectively improve drivers’ performance during driving period. However, both the emotion data and user satisfaction results indicate that participants experienced an increase in feelings of happiness when they were able to visualize the AI assistant while hearing the audio reminders from the assistant. The study serves as one of the pioneering studies aimed at enhancing the theoretical foundation in the field of automotive user interface design, particularly concerning the design of auditory functions.

Application of Virtual Reality in Automotive Design

Application of Virtual Reality in Automotive Design

Advancements in Nanotechnology for Vehicle Frames: Enhancing Safety and Performance

In recent years, the automotive industry, especially the electric vehicle sector, has made considerable strides with the integration of advanced materials. Nanotechnology, a key player in material science, offers significant potential for improving vehicle performance, with safety being one of the most critical aspects of automotive design. This review examines the utilization of nanomaterials such as carbon nanofibers (CNF) and carbon nanotubes (CNT) in vehicle frames, emphasizing their ability to enhance structural strength, reduce vehicle weight, and improve crash resilience. By leveraging these materials, manufacturers can create lighter, stronger, and more energy-efficient vehicles. This paper also explores recent breakthroughs in nanotechnology applied to automotive frames, discusses the challenges faced in large-scale implementation, and outlines potential future research directions that could further optimize vehicle performance and safety. Through this comprehensive review, the author aims to provide a clear understanding of the current landscape and future potential of nanotechnology in vehicle frame design.

Design and Implementation of FPGA Based Optimized Multicore Processors

The rapid development of system-on-chip (SoC) architectures has led to increasingly complex systems facing issues such as multi-threading failures and potential failures. To address these issues, the project announced the development of a multi-student design that combines machine learning algorithms and strong cryptographic techniques to improve performance and security. Data integrity, authentication, and encryption/decryption using private and public cryptographic keys. Use advanced cryptographic algorithms such as Secure Hash Algorithm (SHA- 256) and Advanced Encryption Standard (AES) to strongly protect sensitive data. These measures ensure data integrity while also preventing unauthorized access. Exchange data on multi-core processor architecture and peripheral devices. This setting optimizes system performance, making the system suitable for real-time and mission-critical applications. Using these resources, the architecture can be efficient in machine learning while maintaining high performance. This integration allows for detection of data changes between hardware and software to ensure seamless operation and interoperability within the system. The best in security, high performance, and flexibility across the enterprise. Keywords-AHB protocol, clock register, FPGA, machine learning, support vector machine, automotive electronics design.

A solution for vibration analysis of truck cabin

This study aims to: (1) apply computer-aided engineering to automotive design, (2) investigate the dynamic load transmission lines causing vibrations in the truck cabin, and (3) analyze and compare vibration characteristics on different road types. A model of a light truck was numerically examined. A frequency response function and a dynamic simulation were used to study how the road surface’s excitation force affects structure vibration. The Multi-body Dynamics simulation was performed to estimate the external force acting on the vehicle. In the case of a flat road at a speed of 60 km/h, the counterforce values almost remain constant, corresponding to 1 G. In the case of a vehicle crossing a hole, at a speed of 30 km/h, the counterforce is about 3.57 and 5.67 G at the front and rear axles, respectively. On a bumpy road, at 30 km/h, the counterforce is about 3.3 G on the front axle and 7.2 G on the rear axle. The FRF analysis shows that the truck cabin’s resonant frequency is 29 to 32 Hz.

The Implementation of Enterprise Resource Planning During the Product Design Process Through the Process of Design Thinking

The implementation of Enterprise Resource Planning (ERP) systems in the product design phase plays a crucial role in modern industries. The product design phase with the design thinking approach produces an innovative product that meets user requirements. The product design process, which begins with capturing user requirements and culminating in a thorough specification of a finished product, requires extensive data and expertise. Iterative product design processes contribute to the complexity of the data that must be managed within an organization. This study involved the implementation of an Enterprise Resource Planning (ERP) software named Odoo within the product design process using a design thinking methodology. By examining the student practicum activities in automotive design, starting from the user survey phase and going all the way to the component design details, a comprehensive ERP system was developed. This system is capable of seamlessly integrating all the data throughout the entire process. Based on the outcomes of testing and assessment, it can be concluded that the modules in Odoo software can be effectively integrated into the product design process. Effective processes in integrating ERP into product design phases can improve production quality and efficiency as well as facilitate greater flexibility and innovation. Implementing ERP throughout the product design phase results in a seamless flow of information, enhanced inventory control, and overall productivity enhancement. This ultimately leads to operational efficiency, competitive advantage, and high user satisfaction in the industry.

REFORM AND EXPLORATION OF AUTOMOBILE DESIGN TEACHING IN HIGHER EDUCATION

With the swift evolution of the automobile design industry, driven by technological advancements and shifting consumer preferences, higher education in automobile design faces an urgent need for reform. The current status quo, often focused on traditional teaching methods and outdated curricula, fails to adequately prepare students for the challenges and opportunities of the modern automotive landscape. Hence, a comprehensive teaching reform is paramount to bridge the gap between academia and industry. This reform should aim to align educational objectives with the latest industry trends, emphasizing practical skills, innovative thinking, and adaptability. It involves integrating cutting-edge technologies, fostering interdisciplinary collaboration, and adopting innovative teaching methodologies. By revamping course structures, incorporating project-based learning, and encouraging participation in design competitions, this reform endeavors to nurture graduates who are not just skilled designers but also creative thinkers capable of shaping the future of automobile design. With these transformative measures in place, higher education in automobile design can better equip students with the knowledge, skills, and mindset needed to thrive in an ever-evolving industry, ultimately contributing to the advancement and innovation of automotive design globally.

Enhanced Drag Force Estimation in Automotive Design: A Surrogate Model Leveraging Limited Full-Order Model Drag Data and Comprehensive Physical Field Integration

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is to take advantage not only of the drag coefficients but also physical fields such as velocity, pressure, and kinetic energy evaluated on a cutting plane in the wake of the vehicle and perpendicular to the road. This additional “augmented” information provides a more accurate and robust prediction of the drag force compared to a standard surface response methodology. As a first step, an original reparametrization of the shape based on combination coefficients of shape principal components is proposed, leading to a low-dimensional representation of the shape space. The second step consists in determining principal components of the x-direction momentum flux through a cutting plane behind the car. The final step is to find the mapping between the reduced shape description and the momentum flux formula to achieve an accurate drag estimation. The resulting surrogate model is a space-parameter separated representation with shape principal component coefficients and spatial modes dedicated to drag-force evaluation. The algorithm can deal with shapes of variable mesh by using an optimal transport procedure that interpolates the fields on a shared reference mesh. The Machine Learning algorithm is challenged on a car concept with a three-dimensional shape design space. With only two well-chosen samples, the numerical algorithm is able to return a drag surrogate model with reasonable uniform error over the validation dataset. An incremental learning approach involving additional high-fidelity computations is also proposed. The leading algorithm is shown to improve the model accuracy. The study also shows the sensitivity of the results with respect to the initial experimental design. As feedback, we discuss and suggest what appear to be the correct choices of experimental designs for the best results.

Enhancing the Heat Dissipation Efficiency of Computing Units Within Autonomous Driving Systems and Electric Vehicles

With the rapid development of autonomous driving technology and electric vehicles, the demand for computing units in vehicles has surged, particularly for high-density computing related to artificial intelligence, sensor fusion, and real-time data processing. This increase has posed significant heat dissipation challenges for automotive electronic systems. Effective heat dissipation is essential for ensuring system stability, safety, and extending the lifespan of these systems. This study explores the thermal design of computing units in current autonomous driving systems and electric vehicles. The electronic components within the computing units generate significant heat, but due to environmental constraints, fans can not be used for internal cooling, as their operation can draw in dust from the environment, contaminating the internal electronic components. Therefore, computing units are often designed with a sealed structure, utilizing the thermal conductivity of metal casings to quickly dissipate internal heat sources. The results of this study show that comparing embedded cooling modules within the casing to commonly used thermal pads reveals that changing the materials of the cooling module can significantly enhance cooling performance, noticeably reducing the temperature of the internal electronic components and minimizing the risk of overheating. Additionally, this can improve the overall reliability and performance of the system, providing new ideas and application prospects for future automotive thermal design.

Multi-objective ergonomics design model optimization for micro electric cars via response surface methodology

Despite advancements in ergonomic comfort assessments in automotive design, optimizing seat dimensions within the constrained spaces of micro-electric cars presents a substantial challenge. In this study, response surface methodology (RSM) is utilized for the ergonomics design of a micro electric car in the conceptual design phase. Specifically, five critical seat dimensions are analyzed: Seatback Angle, Seat Base Angle, Steering Wheel Height from Car Base, Distance from Seat Base to Pedals, and Distance from Seat Base to Steering Wheel. The analysis took place using a 2-level full factorial design L32 orthogonal array. The optimal dimensions are determined using RSM, such as the mean comfort level and signal-to-noise ratio are maximized, and the standard deviation for multiple drivers is minimized. Three regression models are formulated for the three responses, and their adequacy is checked using different methods. The optimal dimensions are obtained and verified through digital human modeling simulation. This research offers practical guidelines for the ergonomic seating design in micro-electric vehicles, enhancing comfort without compromising space efficiency.

Unveiling the Future of Safety: Cutting-Edge Simulation Testing of e-Kart Bumpers

Abstract This research project proves the importance of simulation technology in developing safe and efficient components for electric Go-Kart and how this approach could impact the future of automotive design and engineering. The development of electric Go-Kart present unique challenges in ensuring safety and performance, given the increasing demand for eco-friendly mobility solutions in motorsports. As the adoption of the electric vehicles grow, the importance of designing robust and reliable safety components, such as bumpers, become critical to protect drivers and enhance vehicle durability. While previous studies have explored Go-Kart safety and performance, they often relied on traditional testing methods that are time-consuming and limited in their ability to simulate complex crash scenarios. Moreover, many studies have not fully addressed the specific challenges associated with the unique dynamics of electric Go-Kart. This research fills these gaps by focusing on testing electric Go-Kart bumpers through advanced computer simulations used to thoroughly examine various crash and impact scenarios to assess the reliability and durability of the front, side and rear bumpers to improve safety and performance. The results of the stress analysis carried out on the electric Go-Kart body showed that the electric go-kart body experienced displacement and safety factors, with the stress experienced being at a point below the yield strength or yield strength so that the body construction was declared safe against the static loading received, so that the test results this provides detailed insight into material strength, structural design, and possible improvements that can be made to reduce the risk of rider injury.

PRINTING A «BRACKET» TYPE PART FOR AN ENERGY-EFFICIENT CAR

Additive technologies play an important role in modern automotive design and manufacturing, with their applications expanding every year. Originally used primarily for prototyping and mock-up production, 3D printing is now increasingly being used to manufacture key automotive components, with some companies offering fully 3D printed vehicles. This article analyzes the possibilities of printing load-bearing structures and the design of a key element - the electric motor mounting bracket for the TOQ car prototype. TOQ Prototype EV is an energy-efficient electric vehicle prototype being developed by the SU Racing Team from Satbayev University. The goal of the project is to create an ultra-light, durable, energy-efficient prototype vehicle with a long range of 1 kWh of energy. The use of new materials and additive technologies opens up the possibility of creating a variety of combinations, both for the production of parts with increased strength and for the use of advanced materials in the interior of the cabin. The process of prototyping an energy-efficient electric vehicle using additive manufacturing illustrates an innovative approach to improving energy efficiency and reducing energy consumption. This article details the process of designing an electric motor bracket using generative design and additive manufacturing. The study aims to evaluate the feasibility of 3D printing the load-bearing and critical elements of an electric vehicle prototype, as well as to simulate the loads when operating an electric motor with a peak power of 2 kW. Keywords: Mechanical engineering, automotive industry, additive manufacturing, 3D printing, industry 4.0, racing car, energy efficiency.

Evaluating Sustainable Materials for Automotive Applications Through Comprehensive MCDM Analysis Using CODAS, COPRAS, VIKOR, and ENTROPY

The automotive industry faces increasing pressure to incorporate sustainable materials in vehicle manufacturing to meet environmental, economic, and performance requirements. This research develops a comprehensive Multi-Criteria Decision-Making (MCDM) framework to evaluate and select sustainable materials for automotive body and instrument panels. The study integrates CODAS, COPRAS, VIKOR, and ENTROPY methods to systematically assess various material alternatives. By considering multiple criteria, including cost, environmental impact, and mechanical properties, the proposed framework provides a holistic evaluation to identify the most suitable materials. The findings contribute to advancing sustainable practices in the automotive industry, offering manufacturers a robust tool for informed decision-making. This approach supports the industry's transition towards environmentally responsible and efficient vehicle production, ensuring that the selected materials meet the stringent demands of modern automotive design.

The Impact of Electric Vehicle Technologies on Future Automotive Design

The Impact of Electric Vehicle Technologies on Future Automotive Design

Comprehensive parametric study and sensitivity analysis of automotive radiators using different water/ethylene glycol mixtures: Toward thermo‐hydraulic performance and heat transfer characteristics optimization

AbstractAutomotive radiators are primarily utilized in vehicles to dissipate the heat generated by the engine block into the surrounding. Utilizing coolants with superior thermophysical properties reduces the engine power consumption and improves the cooling engine performance. Comprehending the correlation between coolant characteristics and its thermohydraulic behavior is essential for advancing this innovative cooling technology. This investigation introduces a detailed parametric study of an automotive radiator using diversified coolant mixtures. Ethylene glycol (EG)/water mixtures, namely, (40:60), (50:50), and (60:40) are utilized as a coolant. The influences of the operational mechanism parameters, that is, inlet coolant temperature, intake air temperature, airflow rate, coolant flowrate, and coolant mixture ratio on the effectiveness, heat transfer, and fluid flow characteristics of the radiator are investigated. A thermohydraulic coupled model, based on the effectiveness‐NTU and thermal resistance theories, are developed for simulation of the investigated radiator. The outcomes revealed that the heat transfer rate is more significantly influenced by the inlet temperatures of the coolant and air than by the flowrate. Findings reveal optimal conditions for radiator design to be a coolant mixture of (40 EG:60 Water), coolant mixture Reynolds number of 1087.5, air Reynolds number of 2175, 11°C air‐intake temperature, and 94.25°C coolant mixture temperature for engine cooling maximization. The findings also indicated optimum mixture yielded the maximum advantage ratio (AR) and heat transfer with lowest pumping power, which achieved 7.94% and 19.30% higher AR compared to (50:50) and (60:40) mixture solutions, respectively. From energy consumption reduction prospective, the optimal EG/water coolant mixture results in a reduction in pumping power by 25.11% and 49.77%, compared to the (50:50) and (60:40) mixtures, respectively, under the same optimal operating conditions. Conclusively, the optimized automotive radiator design explored in this study offers a promising approach to improving vehicle technology and increasing cooling efficiency in internal combustion engines.

Foreword

The Congress of Automotive and Transport Engineering is an annual scientific event initiated in 1990 by the Society of Automotive Engineers of Romania (SIAR) and various Romanian universities with an interest in Automotive and Transport Engineering. This Congress is endorsed by EAEC (European Automotive Engineers Cooperation). The XXXII-nd edition of SIARS’s International Congress with the theme “Automotive and Transport Engineering” and the XVII-th European Automotive Congress was organized for the third time by Department of Mechanical Machines, Equipment and Transportation from the Politehnica University Timisoara. The Congress purpose is to gather members from academia, industry and government affiliated presenting their possibilities for investigations and latest research, to achieve collaborations in the near future in the field of automotive and transport engineering. The Congress hosted 201 participants from Europe, United States of America, Moldova presenting their scientific achievements within plenary sessions and congress sections. Thus, the auditorium had the opportunity to: • Gain insight into the cutting edge automotive and transport industry and ideas for future developments; • Update their skills and knowledge by attending focused technical sessions; • Network with potential new partners, clients and suppliers; • View the latest technology products and services in the technical exhibition. The papers included in this volume were selected by the Scientific Committee amongst the ones presented within the Congress and are covering the latest issues such as advanced powertrain and propulsion, automotive design and testing, terrain vehicles, advanced engineering methods, materials, automotive technology and maintenance, road safety, mobility of things. EAEC-MVT 2022 Student Congress was integrated into the main congress and offered students from around the world the opportunity to participate at an international conference and to present their papers on the main congress topics. This was also the meeting place for the young students (SIAR junior’s members) in an International Congress. We hope that these papers will be of interest to experts from academia and industry around the world and we wish them to create a favourable framework for addressing key issues in the field of Automotive and Road Transport Engineering that concern contemporary society today. Yours faithfully, EAEC-MVT 2022 Proceedings Editors List of Acknowledgement, Organizing Committee, Sponsors are available in this Pdf.

A Queuing Model Based Rapid Evaluation Method for Automotive Control Design

This paper evaluates the ergonomics performance of automotive driving systems through a new computational model, aiming to enhance vehicle control design more cost-effectively than traditional experimental human factors research in the automotive field. Parameters such as spatial coordinates and control dimensions were measured for different driver interaction controls (e.g., hazard light switches, steering wheel buttons) across three typical passenger vehicles. These parameters were integrated into the QN-MHP-U to simulate driver operational behaviors and predict task performance. A computational method was introduced to assess the ergonomic scores of automotive control designs based on the modeling results. The QN-MHP-U provides a systematic and universally applicable solution for evaluating and comparing vehicle control designs within automotive driving systems. This allows automotive designers to assess and improve vehicle control designs from an ergonomic perspective more efficiently in terms of time and economic costs.

Conceptual design of an electric vehicle chassis using topology optimization method

Abstract Nowadays, the chassis represents the most important structural part of the vehicle, that has a great influence on the passenger’s safety. Lately, topology optimization represents a vital part of the automotive design process. The chassis design can improve the stability and rigidity of the vehicle for accurate handling. In this paper is presented the conceptual design of an EV (electric vehicle) chassis generated by using the topology optimization method. The introduction of the paper presents the latest method used in automotive optimization and the theoretical aspects. In the second part, the basic chassis structure is modelled using CAD software. The objective of this optimization study is to decrease the mass of the chassis to a minimum, without affecting the mechanical properties. Two optimization cases are studied using the steel and aluminium allow material at different boundary conditions. At the final of this research, all results are compared, and the conclusions are presented.

Exploring the Need for Reform in Automobile Design Education Across Higher Education Institutions: Global Perspectives

This study explores the imperative for reforming automobile design education in higher education institutions, driven by rapid advancements in the automotive design industry. This study begins by examining the current landscape and emerging trends within the automotive design sector, highlighting the pressing need for educational reform. It then delineates the objectives and trajectory of the proposed teaching reforms, followed by a comprehensive presentation of the specific reform strategies. This research underscores the potential of these educational reforms to enhance students' practical skills and foster innovative thinking. By doing so, this study aims to provide valuable insights into the ongoing development and improvement of automobile design education. This paper's methodology involves a thorough analysis of the automotive design industry's current state and future directions, providing a contextual framework for the proposed educational reforms. This analysis serves as the foundation for identifying key areas that require modifications in the existing curriculum. The study then outlines a series of targeted reform measures, each designed to address specific gaps in the current educational model and to align it more closely with industry needs. A significant focus of this research is on the anticipated outcomes of these reforms, particularly in terms of student skill development. This paper argues that the proposed changes will lead to a marked improvement in students' practical abilities, enabling them to better meet the demands of the evolving automotive design industry. Additionally, these reforms are expected to cultivate students' innovative thinking capabilities, preparing them for the creative challenges inherent in modern automotive design. Ultimately, this study aims to serve as a valuable resource for educators, administrators, and policymakers involved in shaping the future of automobile design education. By providing a comprehensive overview of the need for reform coupled with actionable strategies for implementation, this study seeks to contribute to the ongoing evolution of automotive design education in response to industry advancements.